Quantitative Measurement of Parkinson Disease Progression Using DaTscan Radiomics and Clinical Features With a Machine Learning–Based Approach
Quantitative Measurement of Parkinson Disease Progression Using DaTscan Radiomics and Clinical Features With a Machine Learning–Based Approach
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Original abstract
Parkinson’s disease (PD) is one of the fastest‐growing neurodegenerative disorders, where timely diagnosis is essential for optimizing treatment. In this study, we created a radiomics–MDS‐UPDRS, a robust dataset by integrating DaTscan SPECT radiomics data with the clinical characteristics of MDS‐UPDRS collected from Parkinson’s progression markers initiative (PPMI) to monitor dopamine depletion in the striatum (caudate and putamen) and allow classification and progression analysis of PD. To construct the dataset, the striatum was segmented using a modified K‐means clustering algorithm, extracting 25 radiomics features combined with 59 clinical features. In addition, linear discriminant analysis was used to select 22 significant characteristics, and a four‐way feature selection method was used to identify 30 significant clinical features, resulting in a refined set of 52. Classification with machine learning models improved performance after LDA, achieving over 91% accuracy. We evaluated feature behavior across six PD severity stages and four clinical visits for progression analysis. The clinical features of MDS‐UPDRS were more sensitive to changes in the severity of the initial PD. At the same time, the integrated dataset, radiomics–MDS‐UPDRS, provided more balanced insights, showing a progression of 33.30%–83.30% and 36.36%–45.50% from the first visit to the fourth visit among the clinical and radiomics features and a progression of 73.33%–96.67% and 13.64%–54.55% between the minimal vs mild and minimal vs very severe stage. Our analysis also revealed practical links between progression features and real‐life scenarios, which highlights the practical value of our study for clinical decision‐making.